Overview

Brought to you by YData

Dataset statistics

Number of variables	35
Number of observations	473
Missing cells	2723
Missing cells (%)	16.4%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	129.5 KiB
Average record size in memory	280.3 B

Variable types

Numeric	9
Text	8
Categorical	17
Unsupported	1

Alerts

`Elogio` has constant value "Não"	Constant
`Crítica` has constant value "Não"	Constant
`Repescagem` has constant value "Não"	Constant
`Bonificado` is highly overall correlated with `Motivo da Bonificação`	High correlation
`Canal` is highly overall correlated with `Plano` and 2 other fields	High correlation
`Cidade` is highly overall correlated with `Contratante` and 3 other fields	High correlation
`Contratante` is highly overall correlated with `Cidade` and 6 other fields	High correlation
`Criado` is highly overall correlated with `ID` and 2 other fields	High correlation
`DDD` is highly overall correlated with `Cidade` and 2 other fields	High correlation
`Estado` is highly overall correlated with `Cidade` and 3 other fields	High correlation
`ID` is highly overall correlated with `Criado` and 2 other fields	High correlation
`ID Anunciante` is highly overall correlated with `Contratante` and 2 other fields	High correlation
`ID Campanha` is highly overall correlated with `Contratante` and 1 other fields	High correlation
`ID Cliente` is highly overall correlated with `Motivo da Bonificação` and 1 other fields	High correlation
`ID Contratante` is highly overall correlated with `Cidade` and 6 other fields	High correlation
`Motivo da Bonificação` is highly overall correlated with `Bonificado` and 1 other fields	High correlation
`Plano` is highly overall correlated with `Canal` and 3 other fields	High correlation
`Tipo` is highly overall correlated with `UTM Campaign` and 2 other fields	High correlation
`UTM Campaign` is highly overall correlated with `Canal` and 5 other fields	High correlation
`UTM Content` is highly overall correlated with `Canal` and 5 other fields	High correlation
`UTM Medium` is highly overall correlated with `Tipo` and 3 other fields	High correlation
`UTM Source` is highly overall correlated with `UTM Campaign` and 2 other fields	High correlation
`Unnamed: 34` is highly overall correlated with `Contratante` and 4 other fields	High correlation
`Últ. Atualização` is highly overall correlated with `Criado` and 2 other fields	High correlation
`Cidade` is highly imbalanced (59.6%)	Imbalance
`Estado` is highly imbalanced (74.6%)	Imbalance
`Motivo da Bonificação` is highly imbalanced (62.5%)	Imbalance
`Unnamed: 34` is highly imbalanced (71.0%)	Imbalance
`Plano` has 42 (8.9%) missing values	Missing
`ID Contratante` has 463 (97.9%) missing values	Missing
`Contratante` has 463 (97.9%) missing values	Missing
`ID Campanha` has 42 (8.9%) missing values	Missing
`Campanha` has 42 (8.9%) missing values	Missing
`Mensagem` has 73 (15.4%) missing values	Missing
`Recibo` has 304 (64.3%) missing values	Missing
`Motivo da Bonificação` has 408 (86.3%) missing values	Missing
`Feedback` has 473 (100.0%) missing values	Missing
`UTM Campaign` has 193 (40.8%) missing values	Missing
`UTM Content` has 220 (46.5%) missing values	Missing
`ID` is uniformly distributed	Uniform
`ID` has unique values	Unique
`Feedback` is an unsupported type, check if it needs cleaning or further analysis	Unsupported

Reproduction

Analysis started	2025-03-19 13:02:56.459902
Analysis finished	2025-03-19 13:03:06.378191
Duration	9.92 seconds
Software version	ydata-profiling vv4.15.0
Download configuration	config.json

ID
Real number (ℝ)

High correlation Uniform Unique

Distinct	473
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	829005

Minimum	828769
Maximum	829241
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.8 KiB

Quantile statistics

Minimum	828769
5-th percentile	828792.6
Q1	828887
median	829005
Q3	829123
95-th percentile	829217.4
Maximum	829241
Range	472
Interquartile range (IQR)	236

Descriptive statistics

Standard deviation	136.6876
Coefficient of variation (CV)	0.00016488151
Kurtosis	-1.2
Mean	829005
Median Absolute Deviation (MAD)	118
Skewness	0
Sum	3.9211936 × 10⁸
Variance	18683.5
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
829241	1	0.2%
828769	1	0.2%
828770	1	0.2%
828771	1	0.2%
828772	1	0.2%
828773	1	0.2%
828774	1	0.2%
828775	1	0.2%
828776	1	0.2%
829225	1	0.2%
Other values (463)	463	97.9%

Value	Count	Frequency (%)
828769	1	0.2%
828770	1	0.2%
828771	1	0.2%
828772	1	0.2%
828773	1	0.2%
828774	1	0.2%
828775	1	0.2%
828776	1	0.2%
828777	1	0.2%
828778	1	0.2%

Value	Count	Frequency (%)
829241	1	0.2%
829240	1	0.2%
829239	1	0.2%
829238	1	0.2%
829237	1	0.2%
829236	1	0.2%
829235	1	0.2%
829234	1	0.2%
829233	1	0.2%
829232	1	0.2%

ID Cliente
Real number (ℝ)

High correlation

Distinct	263
Distinct (%)	55.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	461769.34

Minimum	1888
Maximum	478153
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.8 KiB

Quantile statistics

Minimum	1888
5-th percentile	436568
Q1	477869
median	478002
Q3	478055
95-th percentile	478131
Maximum	478153
Range	476265
Interquartile range (IQR)	186

Descriptive statistics

Standard deviation	68162.041
Coefficient of variation (CV)	0.14761058
Kurtosis	29.930371
Mean	461769.34
Median Absolute Deviation (MAD)	74
Skewness	-5.4116632
Sum	2.184169 × 10⁸
Variance	4.6460639 × 10⁹
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
478020	59	12.5%
445525	22	4.7%
477928	10	2.1%
477711	6	1.3%
477938	6	1.3%
436568	5	1.1%
478035	5	1.1%
477880	4	0.8%
476919	4	0.8%
462038	4	0.8%
Other values (253)	348	73.6%

Value	Count	Frequency (%)
1888	2	0.4%
5368	1	0.2%
15817	1	0.2%
16908	1	0.2%
46718	1	0.2%
55900	2	0.4%
158796	1	0.2%
164947	2	0.4%
183741	1	0.2%
255321	1	0.2%

Value	Count	Frequency (%)
478153	1	0.2%
478152	1	0.2%
478150	2	0.4%
478149	1	0.2%
478148	1	0.2%
478147	1	0.2%
478146	3	0.6%
478145	2	0.4%
478143	1	0.2%
478142	1	0.2%

Cliente
Text

Distinct	253
Distinct (%)	53.5%
Missing	0
Missing (%)	0.0%
Memory size	3.8 KiB

Length

Max length	52
Median length	32
Mean length	11.89852
Min length	2

Characters and Unicode

Total characters	5628
Distinct characters	66
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	168 ?
Unique (%)	35.5%

Sample

1st row	Juliana Barbour
2nd row	Márcia
3rd row	Aline Rodrigues
4th row	Paloma Herbst
5th row	Luiz Fernando

Value	Count	Frequency (%)
ângela	60	7.0%
fernanda	32	3.7%
de	26	3.0%
santos	14	1.6%
ribeiro	12	1.4%
bruno	11	1.3%
eliana	10	1.2%
karina	10	1.2%
brabo	10	1.2%
oliveira	9	1.0%
Other values (368)	665	77.4%

Most occurring characters

Value	Count	Frequency (%)
a	792	14.1%
e	524	9.3%
i	448	8.0%
n	431	7.7%
	386	6.9%
r	385	6.8%
l	335	6.0%
o	325	5.8%
s	207	3.7%
d	144	2.6%
Other values (56)	1651	29.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	5628	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	792	14.1%
e	524	9.3%
i	448	8.0%
n	431	7.7%
	386	6.9%
r	385	6.8%
l	335	6.0%
o	325	5.8%
s	207	3.7%
d	144	2.6%
Other values (56)	1651	29.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	5628	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	792	14.1%
e	524	9.3%
i	448	8.0%
n	431	7.7%
	386	6.9%
r	385	6.8%
l	335	6.0%
o	325	5.8%
s	207	3.7%
d	144	2.6%
Other values (56)	1651	29.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	5628	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	792	14.1%
e	524	9.3%
i	448	8.0%
n	431	7.7%
	386	6.9%
r	385	6.8%
l	335	6.0%
o	325	5.8%
s	207	3.7%
d	144	2.6%
Other values (56)	1651	29.3%

E-mail
Text

Distinct	263
Distinct (%)	55.6%
Missing	0
Missing (%)	0.0%
Memory size	3.8 KiB

Length

Max length	37
Median length	31
Mean length	23.805497
Min length	11

Characters and Unicode

Total characters	11260
Distinct characters	40
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	177 ?
Unique (%)	37.4%

Sample

1st row	juliana@skillsonline.com.br
2nd row	marcinha_mg27@hotmail.com
3rd row	ali.rodrigues@outlook.com.br
4th row	pallomaherbst@gmail.com
5th row	luiz.santos2981@gmail.com

Value	Count	Frequency (%)
apaleari@cusa.canon.com	59	12.5%
febasaglia@hotmail.com	22	4.7%
braboeliana2@gmail.com	10	2.1%
alethearsoliveira@gail.com	6	1.3%
karinabuenotolardo@gmail.com	6	1.3%
dheborafernanda090@gmail.com	5	1.1%
pkkarol375@gmail.com	5	1.1%
karina.dafonsecaborges@gmail.com	4	0.8%
leon.lacerda2015@gmail.com	4	0.8%
dyandraevelyn@gmail.com	4	0.8%
Other values (253)	348	73.6%

Most occurring characters

Value	Count	Frequency (%)
a	1471	13.1%
o	1042	9.3%
m	960	8.5%
i	869	7.7%
l	785	7.0%
c	746	6.6%
.	647	5.7%
e	541	4.8%
r	503	4.5%
@	473	4.2%
Other values (30)	3223	28.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	11260	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	1471	13.1%
o	1042	9.3%
m	960	8.5%
i	869	7.7%
l	785	7.0%
c	746	6.6%
.	647	5.7%
e	541	4.8%
r	503	4.5%
@	473	4.2%
Other values (30)	3223	28.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	11260	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	1471	13.1%
o	1042	9.3%
m	960	8.5%
i	869	7.7%
l	785	7.0%
c	746	6.6%
.	647	5.7%
e	541	4.8%
r	503	4.5%
@	473	4.2%
Other values (30)	3223	28.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	11260	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	1471	13.1%
o	1042	9.3%
m	960	8.5%
i	869	7.7%
l	785	7.0%
c	746	6.6%
.	647	5.7%
e	541	4.8%
r	503	4.5%
@	473	4.2%
Other values (30)	3223	28.6%

DDD
Real number (ℝ)

High correlation

Distinct	29
Distinct (%)	6.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	20.054968

Minimum	11
Maximum	98
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.8 KiB

Quantile statistics

Minimum	11
5-th percentile	11
Q1	11
median	11
Q3	12
95-th percentile	71
Maximum	98
Range	87
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	20.794218
Coefficient of variation (CV)	1.0368612
Kurtosis	4.2002592
Mean	20.054968
Median Absolute Deviation (MAD)	0
Skewness	2.338966
Sum	9486
Variance	432.39951
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=29)

Value	Count	Frequency (%)
11	352	74.4%
21	16	3.4%
61	13	2.7%
71	11	2.3%
19	9	1.9%
92	9	1.9%
31	8	1.7%
15	6	1.3%
12	5	1.1%
62	5	1.1%
Other values (19)	39	8.2%

Value	Count	Frequency (%)
11	352	74.4%
12	5	1.1%
13	4	0.8%
14	1	0.2%
15	6	1.3%
16	1	0.2%
18	3	0.6%
19	9	1.9%
21	16	3.4%
27	4	0.8%

Value	Count	Frequency (%)
98	2	0.4%
92	9	1.9%
89	1	0.2%
85	4	0.8%
83	1	0.2%
82	2	0.4%
81	1	0.2%
75	1	0.2%
71	11	2.3%
67	3	0.6%

Telefone
Real number (ℝ)

Distinct	262
Distinct (%)	55.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	9.7402631 × 10⁸

Minimum	9.1037843 × 10⁸
Maximum	1 × 10⁹
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.8 KiB

Quantile statistics

Minimum	9.1037843 × 10⁸
5-th percentile	9.4319536 × 10⁸
Q1	9.5977858 × 10⁸
median	9.7655486 × 10⁸
Q3	9.8934883 × 10⁸
95-th percentile	9.9933871 × 10⁸
Maximum	1 × 10⁹
Range	89621573
Interquartile range (IQR)	29570257

Descriptive statistics

Standard deviation	18731917
Coefficient of variation (CV)	0.019231428
Kurtosis	0.13578572
Mean	9.7402631 × 10⁸
Median Absolute Deviation (MAD)	16324972
Skewness	-0.67239795
Sum	4.6071445 × 10¹¹
Variance	3.508847 × 10¹⁴
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
959778575	59	12.5%
971890699	22	4.7%
958382589	10	2.1%
986351223	6	1.3%
943195362	6	1.3%
970666784	5	1.1%
984668015	5	1.1%
982441170	4	0.8%
951339972	4	0.8%
953248804	4	0.8%
Other values (252)	348	73.6%

Value	Count	Frequency (%)
910378426	1	0.2%
913413403	2	0.4%
913561272	1	0.2%
916935898	2	0.4%
918778982	2	0.4%
930032732	1	0.2%
930122220	1	0.2%
930323570	3	0.6%
930653452	1	0.2%
940081036	2	0.4%

Value	Count	Frequency (%)
999999999	1	0.2%
999994444	2	0.4%
999937332	3	0.6%
999928080	2	0.4%
999905468	1	0.2%
999898979	2	0.4%
999843963	1	0.2%
999816442	3	0.6%
999804113	2	0.4%
999749199	1	0.2%

ID Imóvel
Real number (ℝ)

Distinct	333
Distinct (%)	70.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5751.1121

Minimum	225
Maximum	8286
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.8 KiB

Quantile statistics

Minimum	225
5-th percentile	2072
Q1	3899
median	6510
Q3	7414
95-th percentile	8058.8
Maximum	8286
Range	8061
Interquartile range (IQR)	3515

Descriptive statistics

Standard deviation	2028.3755
Coefficient of variation (CV)	0.35269274
Kurtosis	-0.77326941
Mean	5751.1121
Median Absolute Deviation (MAD)	1322
Skewness	-0.6740833
Sum	2720276
Variance	4114307.1
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
7918	6	1.3%
7987	5	1.1%
6582	5	1.1%
4721	5	1.1%
5442	5	1.1%
7560	5	1.1%
5015	4	0.8%
7415	4	0.8%
6998	4	0.8%
3850	4	0.8%
Other values (323)	426	90.1%

Value	Count	Frequency (%)
225	1	0.2%
896	2	0.4%
1049	3	0.6%
1176	1	0.2%
1295	1	0.2%
1315	1	0.2%
1331	1	0.2%
1335	1	0.2%
1365	1	0.2%
1431	1	0.2%

Value	Count	Frequency (%)
8286	2	0.4%
8279	1	0.2%
8254	2	0.4%
8245	1	0.2%
8241	1	0.2%
8234	1	0.2%
8231	1	0.2%
8227	1	0.2%
8218	1	0.2%
8215	1	0.2%

Imóvel
Text

Distinct	333
Distinct (%)	70.4%
Missing	0
Missing (%)	0.0%
Memory size	3.8 KiB

Length

Max length	43
Median length	30
Mean length	17.919662
Min length	3

Characters and Unicode

Total characters	8476
Distinct characters	87
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	249 ?
Unique (%)	52.6%

Sample

1st row	Reserva das Árvores - Paineiras
2nd row	Residencial Pedra de Guaratiba
3rd row	Nostro Mooca
4th row	Reserva Direcional Limão
5th row	Residencial Turim

Value	Count	Frequency (%)
vila	41	3.0%
by	35	2.5%
	20	1.4%
living	19	1.4%
da	17	1.2%
sensia	15	1.1%
ipiranga	14	1.0%
vivaz	13	0.9%
mooca	13	0.9%
clube	13	0.9%
Other values (505)	1188	85.6%

Most occurring characters

Value	Count	Frequency (%)
a	999	11.8%
	914	10.8%
i	657	7.8%
o	585	6.9%
e	553	6.5%
r	446	5.3%
n	412	4.9%
l	305	3.6%
s	301	3.6%
t	276	3.3%
Other values (77)	3028	35.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	8476	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	999	11.8%
	914	10.8%
i	657	7.8%
o	585	6.9%
e	553	6.5%
r	446	5.3%
n	412	4.9%
l	305	3.6%
s	301	3.6%
t	276	3.3%
Other values (77)	3028	35.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	8476	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	999	11.8%
	914	10.8%
i	657	7.8%
o	585	6.9%
e	553	6.5%
r	446	5.3%
n	412	4.9%
l	305	3.6%
s	301	3.6%
t	276	3.3%
Other values (77)	3028	35.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	8476	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	999	11.8%
	914	10.8%
i	657	7.8%
o	585	6.9%
e	553	6.5%
r	446	5.3%
n	412	4.9%
l	305	3.6%
s	301	3.6%
t	276	3.3%
Other values (77)	3028	35.7%

Plano
Categorical

High correlation Missing

Distinct	2
Distinct (%)	0.5%
Missing	42
Missing (%)	8.9%
Memory size	3.8 KiB

Business	269
Brokers	162

Length

Max length	8
Median length	8
Mean length	7.6241299
Min length	7

Characters and Unicode

Total characters	3286
Distinct characters	9
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Brokers
2nd row	Business
3rd row	Business
4th row	Business
5th row	Business

Common Values

Value	Count	Frequency (%)
Business	269	56.9%
Brokers	162	34.2%
(Missing)	42	8.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
business	269	62.4%
brokers	162	37.6%

Most occurring characters

Value	Count	Frequency (%)
s	969	29.5%
B	431	13.1%
e	431	13.1%
r	324	9.9%
u	269	8.2%
n	269	8.2%
i	269	8.2%
o	162	4.9%
k	162	4.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3286	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
s	969	29.5%
B	431	13.1%
e	431	13.1%
r	324	9.9%
u	269	8.2%
n	269	8.2%
i	269	8.2%
o	162	4.9%
k	162	4.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3286	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
s	969	29.5%
B	431	13.1%
e	431	13.1%
r	324	9.9%
u	269	8.2%
n	269	8.2%
i	269	8.2%
o	162	4.9%
k	162	4.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3286	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
s	969	29.5%
B	431	13.1%
e	431	13.1%
r	324	9.9%
u	269	8.2%
n	269	8.2%
i	269	8.2%
o	162	4.9%
k	162	4.9%

Cidade
Categorical

High correlation Imbalance

Distinct	38
Distinct (%)	8.0%
Missing	0
Missing (%)	0.0%
Memory size	3.8 KiB

São Paulo	344
Rio de Janeiro	14
Salvador	12
Osasco	12
Manaus	10
Other values (33)	81

Length

Max length	21
Median length	9
Mean length	9.05074
Min length	5

Characters and Unicode

Total characters	4281
Distinct characters	45
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	12 ?
Unique (%)	2.5%

Sample

1st row	Cotia
2nd row	Rio de Janeiro
3rd row	São Paulo
4th row	São Paulo
5th row	Curitiba

Common Values

Value	Count	Frequency (%)
São Paulo	344	72.7%
Rio de Janeiro	14	3.0%
Salvador	12	2.5%
Osasco	12	2.5%
Manaus	10	2.1%
Guarulhos	9	1.9%
Santo André	7	1.5%
Fortaleza	4	0.8%
Carapicuíba	4	0.8%
Goiânia	4	0.8%
Other values (28)	53	11.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
são	350	39.9%
paulo	344	39.2%
rio	14	1.6%
de	14	1.6%
janeiro	14	1.6%
salvador	12	1.4%
osasco	12	1.4%
manaus	10	1.1%
guarulhos	9	1.0%
santo	7	0.8%
Other values (43)	91	10.4%

Most occurring characters

Value	Count	Frequency (%)
o	823	19.2%
a	512	12.0%
	404	9.4%
u	391	9.1%
l	383	8.9%
S	377	8.8%
ã	352	8.2%
P	349	8.2%
r	80	1.9%
i	75	1.8%
Other values (35)	535	12.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	4281	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
o	823	19.2%
a	512	12.0%
	404	9.4%
u	391	9.1%
l	383	8.9%
S	377	8.8%
ã	352	8.2%
P	349	8.2%
r	80	1.9%
i	75	1.8%
Other values (35)	535	12.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	4281	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
o	823	19.2%
a	512	12.0%
	404	9.4%
u	391	9.1%
l	383	8.9%
S	377	8.8%
ã	352	8.2%
P	349	8.2%
r	80	1.9%
i	75	1.8%
Other values (35)	535	12.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	4281	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
o	823	19.2%
a	512	12.0%
	404	9.4%
u	391	9.1%
l	383	8.9%
S	377	8.8%
ã	352	8.2%
P	349	8.2%
r	80	1.9%
i	75	1.8%
Other values (35)	535	12.5%

Estado
Categorical

High correlation Imbalance

Distinct	15
Distinct (%)	3.2%
Missing	0
Missing (%)	0.0%
Memory size	3.8 KiB

São Paulo	410
Rio de Janeiro	16
Bahia	12
Amazonas	10
Ceará	4
Other values (10)	21

Length

Max length	17
Median length	9
Mean length	9.0909091
Min length	5

Characters and Unicode

Total characters	4300
Distinct characters	34
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4 ?
Unique (%)	0.8%

Sample

1st row	São Paulo
2nd row	Rio de Janeiro
3rd row	São Paulo
4th row	São Paulo
5th row	Paraná

Common Values

Value	Count	Frequency (%)
São Paulo	410	86.7%
Rio de Janeiro	16	3.4%
Bahia	12	2.5%
Amazonas	10	2.1%
Ceará	4	0.8%
Goiás	4	0.8%
Rio Grande do Sul	3	0.6%
Distrito Federal	3	0.6%
Minas Gerais	3	0.6%
Alagoas	2	0.4%
Other values (5)	6	1.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
são	410	44.0%
paulo	410	44.0%
rio	19	2.0%
de	16	1.7%
janeiro	16	1.7%
bahia	12	1.3%
amazonas	10	1.1%
ceará	4	0.4%
goiás	4	0.4%
grande	3	0.3%
Other values (14)	28	3.0%

Most occurring characters

Value	Count	Frequency (%)
o	880	20.5%
a	504	11.7%
	459	10.7%
l	418	9.7%
S	415	9.7%
P	414	9.6%
u	414	9.6%
ã	410	9.5%
i	65	1.5%
e	49	1.1%
Other values (24)	272	6.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	4300	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
o	880	20.5%
a	504	11.7%
	459	10.7%
l	418	9.7%
S	415	9.7%
P	414	9.6%
u	414	9.6%
ã	410	9.5%
i	65	1.5%
e	49	1.1%
Other values (24)	272	6.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	4300	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
o	880	20.5%
a	504	11.7%
	459	10.7%
l	418	9.7%
S	415	9.7%
P	414	9.6%
u	414	9.6%
ã	410	9.5%
i	65	1.5%
e	49	1.1%
Other values (24)	272	6.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	4300	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
o	880	20.5%
a	504	11.7%
	459	10.7%
l	418	9.7%
S	415	9.7%
P	414	9.6%
u	414	9.6%
ã	410	9.5%
i	65	1.5%
e	49	1.1%
Other values (24)	272	6.3%

ID Anunciante
Real number (ℝ)

High correlation

Distinct	139
Distinct (%)	29.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1161.0254

Minimum	47
Maximum	2551
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.8 KiB

Quantile statistics

Minimum	47
5-th percentile	111
Q1	355
median	1007
Q3	1991
95-th percentile	2208
Maximum	2551
Range	2504
Interquartile range (IQR)	1636

Descriptive statistics

Standard deviation	794.06218
Coefficient of variation (CV)	0.68393181
Kurtosis	-1.5807245
Mean	1161.0254
Median Absolute Deviation (MAD)	776
Skewness	0.080870425
Sum	549165
Variance	630534.75
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
714	24	5.1%
157	16	3.4%
1472	15	3.2%
1991	15	3.2%
292	14	3.0%
111	13	2.7%
2208	12	2.5%
498	10	2.1%
105	10	2.1%
231	9	1.9%
Other values (129)	335	70.8%

Value	Count	Frequency (%)
47	3	0.6%
103	1	0.2%
105	10	2.1%
111	13	2.7%
133	8	1.7%
146	5	1.1%
155	7	1.5%
157	16	3.4%
160	2	0.4%
180	3	0.6%

Value	Count	Frequency (%)
2551	1	0.2%
2512	2	0.4%
2511	2	0.4%
2500	4	0.8%
2382	1	0.2%
2336	1	0.2%
2334	2	0.4%
2289	1	0.2%
2237	1	0.2%
2229	1	0.2%

Anunciante
Text

Distinct	142
Distinct (%)	30.0%
Missing	0
Missing (%)	0.0%
Memory size	3.8 KiB

Length

Max length	35
Median length	29
Mean length	15.245243
Min length	3

Characters and Unicode

Total characters	7211
Distinct characters	62
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	57 ?
Unique (%)	12.1%

Sample

1st row	Imo House Negócios Imobiliários
2nd row	MRV
3rd row	Emccamp
4th row	Direcional Engenharia
5th row	TNR Construtora

Value	Count	Frequency (%)
de	30	2.8%
engenharia	28	2.7%
incorporadora	26	2.5%
direcional	24	2.3%
da	23	2.2%
barbosa	20	1.9%
silva	20	1.9%
living	16	1.5%
tegra	15	1.4%
residencial	15	1.4%
Other values (253)	839	79.5%

Most occurring characters

Value	Count	Frequency (%)
a	797	11.1%
e	584	8.1%
	583	8.1%
o	561	7.8%
i	529	7.3%
r	526	7.3%
n	440	6.1%
s	342	4.7%
d	264	3.7%
l	242	3.4%
Other values (52)	2343	32.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	7211	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	797	11.1%
e	584	8.1%
	583	8.1%
o	561	7.8%
i	529	7.3%
r	526	7.3%
n	440	6.1%
s	342	4.7%
d	264	3.7%
l	242	3.4%
Other values (52)	2343	32.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	7211	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	797	11.1%
e	584	8.1%
	583	8.1%
o	561	7.8%
i	529	7.3%
r	526	7.3%
n	440	6.1%
s	342	4.7%
d	264	3.7%
l	242	3.4%
Other values (52)	2343	32.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	7211	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	797	11.1%
e	584	8.1%
	583	8.1%
o	561	7.8%
i	529	7.3%
r	526	7.3%
n	440	6.1%
s	342	4.7%
d	264	3.7%
l	242	3.4%
Other values (52)	2343	32.5%

ID Contratante
Categorical

High correlation Missing

Distinct	4
Distinct (%)	40.0%
Missing	463
Missing (%)	97.9%
Memory size	3.8 KiB

5.0	7
144.0	1
159.0	1
1.0	1

Length

Max length	5
Median length	3
Mean length	3.4
Min length	3

Characters and Unicode

Total characters	34
Distinct characters	6
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	30.0%

Sample

1st row	5.0
2nd row	5.0
3rd row	144.0
4th row	5.0
5th row	159.0

Common Values

Value	Count	Frequency (%)
5.0	7	1.5%
144.0	1	0.2%
159.0	1	0.2%
1.0	1	0.2%
(Missing)	463	97.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
5.0	7	70.0%
144.0	1	10.0%
159.0	1	10.0%
1.0	1	10.0%

Most occurring characters

Value	Count	Frequency (%)
.	10	29.4%
0	10	29.4%
5	8	23.5%
1	3	8.8%
4	2	5.9%
9	1	2.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	34	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	10	29.4%
0	10	29.4%
5	8	23.5%
1	3	8.8%
4	2	5.9%
9	1	2.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	34	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	10	29.4%
0	10	29.4%
5	8	23.5%
1	3	8.8%
4	2	5.9%
9	1	2.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	34	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	10	29.4%
0	10	29.4%
5	8	23.5%
1	3	8.8%
4	2	5.9%
9	1	2.9%

Contratante
Categorical

High correlation Missing

Distinct	4
Distinct (%)	40.0%
Missing	463
Missing (%)	97.9%
Memory size	3.8 KiB

Tegra SP	7
REM	1
Grupo Luni - Cumbuca	1
Cyrela SP	1

Length

Max length	20
Median length	8
Mean length	8.8
Min length	3

Characters and Unicode

Total characters	88
Distinct characters	25
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	30.0%

Sample

1st row	Tegra SP
2nd row	Tegra SP
3rd row	REM
4th row	Tegra SP
5th row	Grupo Luni - Cumbuca

Common Values

Value	Count	Frequency (%)
Tegra SP	7	1.5%
REM	1	0.2%
Grupo Luni - Cumbuca	1	0.2%
Cyrela SP	1	0.2%
(Missing)	463	97.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
sp	8	38.1%
tegra	7	33.3%
rem	1	4.8%
grupo	1	4.8%
luni	1	4.8%
	1	4.8%
cumbuca	1	4.8%
cyrela	1	4.8%

Most occurring characters

Value	Count	Frequency (%)
	11	12.5%
r	9	10.2%
a	9	10.2%
P	8	9.1%
e	8	9.1%
S	8	9.1%
T	7	8.0%
g	7	8.0%
u	4	4.5%
C	2	2.3%
Other values (15)	15	17.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	88	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	11	12.5%
r	9	10.2%
a	9	10.2%
P	8	9.1%
e	8	9.1%
S	8	9.1%
T	7	8.0%
g	7	8.0%
u	4	4.5%
C	2	2.3%
Other values (15)	15	17.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	88	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	11	12.5%
r	9	10.2%
a	9	10.2%
P	8	9.1%
e	8	9.1%
S	8	9.1%
T	7	8.0%
g	7	8.0%
u	4	4.5%
C	2	2.3%
Other values (15)	15	17.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	88	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	11	12.5%
r	9	10.2%
a	9	10.2%
P	8	9.1%
e	8	9.1%
S	8	9.1%
T	7	8.0%
g	7	8.0%
u	4	4.5%
C	2	2.3%
Other values (15)	15	17.0%

ID Campanha
Real number (ℝ)

High correlation Missing

Distinct	178
Distinct (%)	41.3%
Missing	42
Missing (%)	8.9%
Infinite	0
Infinite (%)	0.0%
Mean	1628.9374

Minimum	3
Maximum	2201
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.8 KiB

Quantile statistics

Minimum	3
5-th percentile	895
Q1	1371
median	1723
Q3	1943.5
95-th percentile	2138
Maximum	2201
Range	2198
Interquartile range (IQR)	572.5

Descriptive statistics

Standard deviation	439.84763
Coefficient of variation (CV)	0.2700212
Kurtosis	2.9898952
Mean	1628.9374
Median Absolute Deviation (MAD)	300
Skewness	-1.4168486
Sum	702072
Variance	193465.93
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1943	15	3.2%
1828	10	2.1%
3	10	2.1%
1670	8	1.7%
1161	8	1.7%
1493	8	1.7%
1731	8	1.7%
1343	7	1.5%
1370	6	1.3%
1893	6	1.3%
Other values (168)	345	72.9%
(Missing)	42	8.9%

Value	Count	Frequency (%)
3	10	2.1%
113	1	0.2%
117	1	0.2%
451	1	0.2%
736	3	0.6%
753	1	0.2%
759	1	0.2%
861	2	0.4%
887	1	0.2%
895	3	0.6%

Value	Count	Frequency (%)
2201	1	0.2%
2194	1	0.2%
2192	2	0.4%
2186	1	0.2%
2184	1	0.2%
2181	2	0.4%
2174	1	0.2%
2172	2	0.4%
2159	3	0.6%
2157	1	0.2%

Campanha
Text

Missing

Distinct	78
Distinct (%)	18.1%
Missing	42
Missing (%)	8.9%
Memory size	3.8 KiB

Length

Max length	51
Median length	16
Mean length	19.983759
Min length	13

Characters and Unicode

Total characters	8613
Distinct characters	65
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	40 ?
Unique (%)	9.3%

Sample

1st row	Campanha Brokers
2nd row	Campanha Starter
3rd row	Campanha Starter
4th row	Campanha Starter - São Paulo
5th row	Campanha Starter

Value	Count	Frequency (%)
campanha	206	14.4%
	181	12.6%
starter	130	9.1%
de	102	7.1%
leads	100	7.0%
geração	100	7.0%
produto	78	5.4%
brokers	62	4.3%
turbo	27	1.9%
grupo	25	1.7%
Other values (150)	422	29.4%

Most occurring characters

Value	Count	Frequency (%)
a	1292	15.0%
	1008	11.7%
r	776	9.0%
e	637	7.4%
o	571	6.6%
t	397	4.6%
d	319	3.7%
n	298	3.5%
C	256	3.0%
s	251	2.9%
Other values (55)	2808	32.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	8613	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	1292	15.0%
	1008	11.7%
r	776	9.0%
e	637	7.4%
o	571	6.6%
t	397	4.6%
d	319	3.7%
n	298	3.5%
C	256	3.0%
s	251	2.9%
Other values (55)	2808	32.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	8613	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	1292	15.0%
	1008	11.7%
r	776	9.0%
e	637	7.4%
o	571	6.6%
t	397	4.6%
d	319	3.7%
n	298	3.5%
C	256	3.0%
s	251	2.9%
Other values (55)	2808	32.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	8613	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	1292	15.0%
	1008	11.7%
r	776	9.0%
e	637	7.4%
o	571	6.6%
t	397	4.6%
d	319	3.7%
n	298	3.5%
C	256	3.0%
s	251	2.9%
Other values (55)	2808	32.6%

Tipo
Categorical

High correlation

Distinct	3
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Memory size	3.8 KiB

Grupo	368
Produto	78
Turbo Leads	27

Length

Max length	11
Median length	5
Mean length	5.6723044
Min length	5

Characters and Unicode

Total characters	2683
Distinct characters	15
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Grupo
2nd row	Grupo
3rd row	Grupo
4th row	Grupo
5th row	Grupo

Common Values

Value	Count	Frequency (%)
Grupo	368	77.8%
Produto	78	16.5%
Turbo Leads	27	5.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
grupo	368	73.6%
produto	78	15.6%
turbo	27	5.4%
leads	27	5.4%

Most occurring characters

Value	Count	Frequency (%)
o	551	20.5%
u	473	17.6%
r	473	17.6%
G	368	13.7%
p	368	13.7%
d	105	3.9%
P	78	2.9%
t	78	2.9%
T	27	1.0%
b	27	1.0%
Other values (5)	135	5.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2683	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
o	551	20.5%
u	473	17.6%
r	473	17.6%
G	368	13.7%
p	368	13.7%
d	105	3.9%
P	78	2.9%
t	78	2.9%
T	27	1.0%
b	27	1.0%
Other values (5)	135	5.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2683	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
o	551	20.5%
u	473	17.6%
r	473	17.6%
G	368	13.7%
p	368	13.7%
d	105	3.9%
P	78	2.9%
t	78	2.9%
T	27	1.0%
b	27	1.0%
Other values (5)	135	5.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2683	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
o	551	20.5%
u	473	17.6%
r	473	17.6%
G	368	13.7%
p	368	13.7%
d	105	3.9%
P	78	2.9%
t	78	2.9%
T	27	1.0%
b	27	1.0%
Other values (5)	135	5.0%

Mensagem
Text

Missing

Distinct	376
Distinct (%)	94.0%
Missing	73
Missing (%)	15.4%
Memory size	3.8 KiB

Length

Max length	300
Median length	266
Mean length	242.7425
Min length	94

Characters and Unicode

Total characters	97097
Distinct characters	94
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	355 ?
Unique (%)	88.8%

Sample

1st row	Olá! Meu nome é Juliana Barbour, email juliana@skillsonline.com.br e telefone (11) 95081-0347. Eu vi o Reserva das Árvores - Paineiras no Apto e me interessei: https://apto.vc/br/sp/cotia/novo-lageado/reserva-das-arvores-paineiras Gostaria de tirar uma dúvida com você. :)
2nd row	Olá! Meu nome é Márcia, email marcinha_mg27@hotmail.com e telefone (21) 98868-9981. Eu vi o Residencial Pedra de Guaratiba no Apto e me interessei: https://apto.vc/br/rj/rio-de-janeiro/guaratiba/residencial-pedra-de-guaratiba Gostaria de tirar uma dúvida com você. :)
3rd row	Olá! Meu nome é Aline Rodrigues, email ali.rodrigues@outlook.com.br e telefone (11) 97655-4856. Eu vi o Nostro Mooca no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/mooca/nostro-mooca Gostaria de tirar uma dúvida com você. :)
4th row	Olá! Meu nome é Paloma Herbst, email pallomaherbst@gmail.com e telefone (11) 99695-7095. Eu vi o Reserva Direcional Limão no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/limao/reserva-direcional-limao Gostaria de tirar uma dúvida com você. :)
5th row	Olá! Meu nome é Luiz Fernando, email luiz.santos2981@gmail.com e telefone (41) 99721-4095. Eu vi o Residencial Turim no Apto e me interessei: https://apto.vc/br/pr/curitiba/portao/residencial-turim Gostaria de tirar uma dúvida com você. :)

Value	Count	Frequency (%)
e	788	6.2%
de	429	3.4%
no	424	3.4%
	396	3.1%
apto	392	3.1%
meu	389	3.1%
o	389	3.1%
eu	388	3.1%
vi	388	3.1%
olá	388	3.1%
Other values (1578)	8270	65.4%

Most occurring characters

Value	Count	Frequency (%)
	12241	12.6%
a	7805	8.0%
e	7407	7.6%
o	6798	7.0%
i	5473	5.6%
t	4148	4.3%
r	3900	4.0%
s	3546	3.7%
n	3339	3.4%
m	3327	3.4%
Other values (84)	39113	40.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	97097	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	12241	12.6%
a	7805	8.0%
e	7407	7.6%
o	6798	7.0%
i	5473	5.6%
t	4148	4.3%
r	3900	4.0%
s	3546	3.7%
n	3339	3.4%
m	3327	3.4%
Other values (84)	39113	40.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	97097	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	12241	12.6%
a	7805	8.0%
e	7407	7.6%
o	6798	7.0%
i	5473	5.6%
t	4148	4.3%
r	3900	4.0%
s	3546	3.7%
n	3339	3.4%
m	3327	3.4%
Other values (84)	39113	40.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	97097	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	12241	12.6%
a	7805	8.0%
e	7407	7.6%
o	6798	7.0%
i	5473	5.6%
t	4148	4.3%
r	3900	4.0%
s	3546	3.7%
n	3339	3.4%
m	3327	3.4%
Other values (84)	39113	40.3%

Canal
Categorical

High correlation

Distinct	4
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Memory size	3.8 KiB

WhatsApp	248
Mensagem	140
Chat	73
Agendamento	12

Length

Max length	11
Median length	8
Mean length	7.4587738
Min length	4

Characters and Unicode

Total characters	3528
Distinct characters	15
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	WhatsApp
2nd row	WhatsApp
3rd row	WhatsApp
4th row	WhatsApp
5th row	WhatsApp

Common Values

Value	Count	Frequency (%)
WhatsApp	248	52.4%
Mensagem	140	29.6%
Chat	73	15.4%
Agendamento	12	2.5%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
whatsapp	248	52.4%
mensagem	140	29.6%
chat	73	15.4%
agendamento	12	2.5%

Most occurring characters

Value	Count	Frequency (%)
p	496	14.1%
a	473	13.4%
s	388	11.0%
t	333	9.4%
h	321	9.1%
e	304	8.6%
A	260	7.4%
W	248	7.0%
n	164	4.6%
g	152	4.3%
Other values (5)	389	11.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3528	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
p	496	14.1%
a	473	13.4%
s	388	11.0%
t	333	9.4%
h	321	9.1%
e	304	8.6%
A	260	7.4%
W	248	7.0%
n	164	4.6%
g	152	4.3%
Other values (5)	389	11.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3528	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
p	496	14.1%
a	473	13.4%
s	388	11.0%
t	333	9.4%
h	321	9.1%
e	304	8.6%
A	260	7.4%
W	248	7.0%
n	164	4.6%
g	152	4.3%
Other values (5)	389	11.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3528	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
p	496	14.1%
a	473	13.4%
s	388	11.0%
t	333	9.4%
h	321	9.1%
e	304	8.6%
A	260	7.4%
W	248	7.0%
n	164	4.6%
g	152	4.3%
Other values (5)	389	11.0%

Recibo
Text

Missing

Distinct	164
Distinct (%)	97.0%
Missing	304
Missing (%)	64.3%
Memory size	3.8 KiB

Length

Max length	255
Median length	68
Mean length	34.331361
Min length	13

Characters and Unicode

Total characters	5802
Distinct characters	77
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	160 ?
Unique (%)	94.7%

Sample

1st row	Hypnobox V.3: 871907
2nd row	SalesForce Direcional: 2025-03-18 08:06:00
3rd row	SalesForce Direcional: 2025-03-18 08:20:33
4th row	Tenda Conecta: bc9c211a-01e5-41e3-b8aa-9bac20e7fb1b
5th row	Hubspot Lucio: Obrigado por enviar o formulário. 2025-03-18 08:55:35

Value	Count	Frequency (%)
hypnobox	41	6.3%
2025-03-18	36	5.5%
v.3	35	5.4%
salesforce	27	4.2%
direcional	24	3.7%
leads	23	3.5%
cyrela	18	2.8%
id	14	2.2%
studio	13	2.0%
cv	13	2.0%
Other values (247)	405	62.4%

Most occurring characters

Value	Count	Frequency (%)
	480	8.3%
o	301	5.2%
a	273	4.7%
e	271	4.7%
:	262	4.5%
2	245	4.2%
0	241	4.2%
3	209	3.6%
1	207	3.6%
5	185	3.2%
Other values (67)	3128	53.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	5802	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	480	8.3%
o	301	5.2%
a	273	4.7%
e	271	4.7%
:	262	4.5%
2	245	4.2%
0	241	4.2%
3	209	3.6%
1	207	3.6%
5	185	3.2%
Other values (67)	3128	53.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	5802	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	480	8.3%
o	301	5.2%
a	273	4.7%
e	271	4.7%
:	262	4.5%
2	245	4.2%
0	241	4.2%
3	209	3.6%
1	207	3.6%
5	185	3.2%
Other values (67)	3128	53.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	5802	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	480	8.3%
o	301	5.2%
a	273	4.7%
e	271	4.7%
:	262	4.5%
2	245	4.2%
0	241	4.2%
3	209	3.6%
1	207	3.6%
5	185	3.2%
Other values (67)	3128	53.9%

Bonificado
Categorical

High correlation

Distinct	2
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	3.8 KiB

Não	408
Sim	65

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	1419
Distinct characters	6
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Não
2nd row	Não
3rd row	Não
4th row	Não
5th row	Não

Common Values

Value	Count	Frequency (%)
Não	408	86.3%
Sim	65	13.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
não	408	86.3%
sim	65	13.7%

Most occurring characters

Value	Count	Frequency (%)
N	408	28.8%
ã	408	28.8%
o	408	28.8%
S	65	4.6%
i	65	4.6%
m	65	4.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1419	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	408	28.8%
ã	408	28.8%
o	408	28.8%
S	65	4.6%
i	65	4.6%
m	65	4.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1419	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	408	28.8%
ã	408	28.8%
o	408	28.8%
S	65	4.6%
i	65	4.6%
m	65	4.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1419	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	408	28.8%
ã	408	28.8%
o	408	28.8%
S	65	4.6%
i	65	4.6%
m	65	4.6%

Motivo da Bonificação
Categorical

High correlation Imbalance Missing

Distinct	4
Distinct (%)	6.2%
Missing	408
Missing (%)	86.3%
Memory size	3.8 KiB

Bonificação automática por fidelidade	56
Teste	6
Possível erro de integração	2
Aluguel / Busca Locação	1

Length

Max length	37
Median length	37
Mean length	33.523077
Min length	5

Characters and Unicode

Total characters	2179
Distinct characters	28
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	1.5%

Sample

1st row	Bonificação automática por fidelidade
2nd row	Bonificação automática por fidelidade
3rd row	Bonificação automática por fidelidade
4th row	Bonificação automática por fidelidade
5th row	Bonificação automática por fidelidade

Common Values

Value	Count	Frequency (%)
Bonificação automática por fidelidade	56	11.8%
Teste	6	1.3%
Possível erro de integração	2	0.4%
Aluguel / Busca Locação	1	0.2%
(Missing)	408	86.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
bonificação	56	23.1%
automática	56	23.1%
por	56	23.1%
fidelidade	56	23.1%
teste	6	2.5%
possível	2	0.8%
erro	2	0.8%
de	2	0.8%
integração	2	0.8%
aluguel	1	0.4%
Other values (3)	3	1.2%

Most occurring characters

Value	Count	Frequency (%)
i	282	12.9%
o	232	10.6%
a	228	10.5%
	177	8.1%
d	170	7.8%
e	133	6.1%
t	120	5.5%
c	114	5.2%
f	112	5.1%
r	62	2.8%
Other values (18)	549	25.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2179	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
i	282	12.9%
o	232	10.6%
a	228	10.5%
	177	8.1%
d	170	7.8%
e	133	6.1%
t	120	5.5%
c	114	5.2%
f	112	5.1%
r	62	2.8%
Other values (18)	549	25.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2179	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
i	282	12.9%
o	232	10.6%
a	228	10.5%
	177	8.1%
d	170	7.8%
e	133	6.1%
t	120	5.5%
c	114	5.2%
f	112	5.1%
r	62	2.8%
Other values (18)	549	25.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2179	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
i	282	12.9%
o	232	10.6%
a	228	10.5%
	177	8.1%
d	170	7.8%
e	133	6.1%
t	120	5.5%
c	114	5.2%
f	112	5.1%
r	62	2.8%
Other values (18)	549	25.2%

Elogio
Categorical

Constant

Distinct	1
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	3.8 KiB

Não	473

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	1419
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Não
2nd row	Não
3rd row	Não
4th row	Não
5th row	Não

Common Values

Value	Count	Frequency (%)
Não	473	100.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
não	473	100.0%

Most occurring characters

Value	Count	Frequency (%)
N	473	33.3%
ã	473	33.3%
o	473	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1419	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	473	33.3%
ã	473	33.3%
o	473	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1419	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	473	33.3%
ã	473	33.3%
o	473	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1419	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	473	33.3%
ã	473	33.3%
o	473	33.3%

Crítica
Categorical

Constant

Distinct	1
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	3.8 KiB

Não	473

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	1419
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Não
2nd row	Não
3rd row	Não
4th row	Não
5th row	Não

Common Values

Value	Count	Frequency (%)
Não	473	100.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
não	473	100.0%

Most occurring characters

Value	Count	Frequency (%)
N	473	33.3%
ã	473	33.3%
o	473	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1419	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	473	33.3%
ã	473	33.3%
o	473	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1419	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	473	33.3%
ã	473	33.3%
o	473	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1419	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	473	33.3%
ã	473	33.3%
o	473	33.3%

Repescagem
Categorical

Constant

Distinct	1
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	3.8 KiB

Não	473

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	1419
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Não
2nd row	Não
3rd row	Não
4th row	Não
5th row	Não

Common Values

Value	Count	Frequency (%)
Não	473	100.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
não	473	100.0%

Most occurring characters

Value	Count	Frequency (%)
N	473	33.3%
ã	473	33.3%
o	473	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1419	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	473	33.3%
ã	473	33.3%
o	473	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1419	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	473	33.3%
ã	473	33.3%
o	473	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1419	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	473	33.3%
ã	473	33.3%
o	473	33.3%

Feedback
Unsupported

Missing Rejected Unsupported

Missing	473
Missing (%)	100.0%
Memory size	3.8 KiB

Criado
Real number (ℝ)

High correlation

Distinct	445
Distinct (%)	94.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	45734.652

Minimum	45734.325
Maximum	45735.304
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.8 KiB

Quantile statistics

Minimum	45734.325
5-th percentile	45734.382
Q1	45734.492
median	45734.594
Q3	45734.824
95-th percentile	45735.003
Maximum	45735.304
Range	0.97931713
Interquartile range (IQR)	0.33208333

Descriptive statistics

Standard deviation	0.21136959
Coefficient of variation (CV)	4.6216509 × 10^-6
Kurtosis	-0.38271324
Mean	45734.652
Median Absolute Deviation (MAD)	0.1533912
Skewness	0.60868314
Sum	21632490
Variance	0.044677106
Monotonicity	Increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
45734.55721	3	0.6%
45734.55494	2	0.4%
45734.5527	2	0.4%
45734.38924	2	0.4%
45734.9944	2	0.4%
45734.553	2	0.4%
45734.55624	2	0.4%
45734.55532	2	0.4%
45734.55742	2	0.4%
45734.55051	2	0.4%
Other values (435)	452	95.6%

Value	Count	Frequency (%)
45734.32454	1	0.2%
45734.32605	1	0.2%
45734.32771	1	0.2%
45734.33747	1	0.2%
45734.33791	1	0.2%
45734.34492	1	0.2%
45734.34674	1	0.2%
45734.34757	1	0.2%
45734.36199	1	0.2%
45734.36503	1	0.2%

Value	Count	Frequency (%)
45735.30385	1	0.2%
45735.29725	1	0.2%
45735.28907	1	0.2%
45735.26029	1	0.2%
45735.22538	2	0.4%
45735.19372	1	0.2%
45735.17921	1	0.2%
45735.13497	1	0.2%
45735.07955	1	0.2%
45735.07934	1	0.2%

UTM Source
Categorical

High correlation

Distinct	8
Distinct (%)	1.7%
Missing	0
Missing (%)	0.0%
Memory size	3.8 KiB

chatbot	149
direct	123
facebook	70
apto	67
google	59
Other values (3)	5

Length

Max length	20
Median length	14
Mean length	6.3826638
Min length	4

Characters and Unicode

Total characters	3019
Distinct characters	22
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.2%

Sample

1st row	direct
2nd row	google
3rd row	direct
4th row	google
5th row	chatbot

Common Values

Value	Count	Frequency (%)
chatbot	149	31.5%
direct	123	26.0%
facebook	70	14.8%
apto	67	14.2%
google	59	12.5%
Lifull-connect	2	0.4%
bing	2	0.4%
Facebook_Mobile_Feed	1	0.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
chatbot	149	31.5%
direct	123	26.0%
facebook	70	14.8%
apto	67	14.2%
google	59	12.5%
lifull-connect	2	0.4%
bing	2	0.4%
facebook_mobile_feed	1	0.2%

Most occurring characters

Value	Count	Frequency (%)
t	490	16.2%
o	479	15.9%
c	347	11.5%
a	287	9.5%
e	258	8.5%
b	223	7.4%
h	149	4.9%
i	128	4.2%
d	124	4.1%
r	123	4.1%
Other values (12)	411	13.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3019	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
t	490	16.2%
o	479	15.9%
c	347	11.5%
a	287	9.5%
e	258	8.5%
b	223	7.4%
h	149	4.9%
i	128	4.2%
d	124	4.1%
r	123	4.1%
Other values (12)	411	13.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3019	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
t	490	16.2%
o	479	15.9%
c	347	11.5%
a	287	9.5%
e	258	8.5%
b	223	7.4%
h	149	4.9%
i	128	4.2%
d	124	4.1%
r	123	4.1%
Other values (12)	411	13.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3019	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
t	490	16.2%
o	479	15.9%
c	347	11.5%
a	287	9.5%
e	258	8.5%
b	223	7.4%
h	149	4.9%
i	128	4.2%
d	124	4.1%
r	123	4.1%
Other values (12)	411	13.6%

UTM Medium
Categorical

High correlation

Distinct	9
Distinct (%)	1.9%
Missing	0
Missing (%)	0.0%
Memory size	3.8 KiB

wpp	149
none	123
email	67
wpp_leadads	59
cpc	52
Other values (4)	23

Length

Max length	11
Median length	7
Mean length	4.7188161
Min length	3

Characters and Unicode

Total characters	2232
Distinct characters	18
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	none
2nd row	cpc
3rd row	none
4th row	cpc
5th row	wpp

Common Values

Value	Count	Frequency (%)
wpp	149	31.5%
none	123	26.0%
email	67	14.2%
wpp_leadads	59	12.5%
cpc	52	11.0%
leadads	9	1.9%
organic	9	1.9%
display	3	0.6%
CPC	2	0.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
wpp	149	31.5%
none	123	26.0%
email	67	14.2%
wpp_leadads	59	12.5%
cpc	54	11.4%
leadads	9	1.9%
organic	9	1.9%
display	3	0.6%

Most occurring characters

Value	Count	Frequency (%)
p	471	21.1%
e	258	11.6%
n	255	11.4%
a	215	9.6%
w	208	9.3%
d	139	6.2%
l	138	6.2%
o	132	5.9%
c	113	5.1%
i	79	3.5%
Other values (8)	224	10.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2232	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
p	471	21.1%
e	258	11.6%
n	255	11.4%
a	215	9.6%
w	208	9.3%
d	139	6.2%
l	138	6.2%
o	132	5.9%
c	113	5.1%
i	79	3.5%
Other values (8)	224	10.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2232	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
p	471	21.1%
e	258	11.6%
n	255	11.4%
a	215	9.6%
w	208	9.3%
d	139	6.2%
l	138	6.2%
o	132	5.9%
c	113	5.1%
i	79	3.5%
Other values (8)	224	10.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2232	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
p	471	21.1%
e	258	11.6%
n	255	11.4%
a	215	9.6%
w	208	9.3%
d	139	6.2%
l	138	6.2%
o	132	5.9%
c	113	5.1%
i	79	3.5%
Other values (8)	224	10.0%

UTM Campaign
Categorical

High correlation Missing

Distinct	15
Distinct (%)	5.4%
Missing	193
Missing (%)	40.8%
Memory size	3.8 KiB

pgproduto	71
jornadab2c	66
leadads_produto_t1	33
bairros	28
recomendacao_ativa	23
Other values (10)	59

Length

Max length	19
Median length	15
Mean length	10.657143
Min length	4

Characters and Unicode

Total characters	2984
Distinct characters	27
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.4%

Sample

1st row	pg_busca
2nd row	leadads_produto_t1
3rd row	home
4th row	pgproduto
5th row	leadads_produto_t1

Common Values

Value	Count	Frequency (%)
pgproduto	71	15.0%
jornadab2c	66	14.0%
leadads_produto_t1	33	7.0%
bairros	28	5.9%
recomendacao_ativa	23	4.9%
ctwa	15	3.2%
home	15	3.2%
leadads_bairros	12	2.5%
pg_busca	3	0.6%
performance_max	3	0.6%
Other values (5)	11	2.3%
(Missing)	193	40.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
pgproduto	71	25.4%
jornadab2c	66	23.6%
leadads_produto_t1	33	11.8%
bairros	28	10.0%
recomendacao_ativa	23	8.2%
ctwa	15	5.4%
home	15	5.4%
leadads_bairros	12	4.3%
pg_busca	3	1.1%
performance_max	3	1.1%
Other values (5)	11	3.9%

Most occurring characters

Value	Count	Frequency (%)
a	397	13.3%
o	388	13.0%
d	292	9.8%
r	286	9.6%
p	188	6.3%
t	183	6.1%
c	140	4.7%
e	116	3.9%
u	112	3.8%
b	110	3.7%
Other values (17)	772	25.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2984	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	397	13.3%
o	388	13.0%
d	292	9.8%
r	286	9.6%
p	188	6.3%
t	183	6.1%
c	140	4.7%
e	116	3.9%
u	112	3.8%
b	110	3.7%
Other values (17)	772	25.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2984	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	397	13.3%
o	388	13.0%
d	292	9.8%
r	286	9.6%
p	188	6.3%
t	183	6.1%
c	140	4.7%
e	116	3.9%
u	112	3.8%
b	110	3.7%
Other values (17)	772	25.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2984	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	397	13.3%
o	388	13.0%
d	292	9.8%
r	286	9.6%
p	188	6.3%
t	183	6.1%
c	140	4.7%
e	116	3.9%
u	112	3.8%
b	110	3.7%
Other values (17)	772	25.9%

UTM Content
Categorical

High correlation Missing

Distinct	29
Distinct (%)	11.5%
Missing	220
Missing (%)	46.5%
Memory size	3.8 KiB

chat	86
d0	62
mensagem	52
sao_paulo	10
guarulhos	5
Other values (24)	38

Length

Max length	19
Median length	18
Mean length	5.6561265
Min length	2

Characters and Unicode

Total characters	1431
Distinct characters	40
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	13 ?
Unique (%)	5.1%

Sample

1st row	rio_de_janeiro
2nd row	sao_paulo
3rd row	chat
4th row	neo-brooklin
5th row	mensagem

Common Values

Value	Count	Frequency (%)
chat	86	18.2%
d0	62	13.1%
mensagem	52	11.0%
sao_paulo	10	2.1%
guarulhos	5	1.1%
1422913285558099	3	0.6%
2066476867034528	3	0.6%
d1	3	0.6%
starter_plus	2	0.4%
paginas_busca	2	0.4%
Other values (19)	25	5.3%
(Missing)	220	46.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
chat	86	34.0%
d0	62	24.5%
mensagem	52	20.6%
sao_paulo	10	4.0%
guarulhos	5	2.0%
1422913285558099	3	1.2%
2066476867034528	3	1.2%
d1	3	1.2%
starter_plus	2	0.8%
paginas_busca	2	0.8%
Other values (19)	25	9.9%

Most occurring characters

Value	Count	Frequency (%)
a	194	13.6%
e	127	8.9%
m	108	7.5%
t	107	7.5%
c	97	6.8%
h	92	6.4%
s	81	5.7%
0	75	5.2%
d	74	5.2%
n	65	4.5%
Other values (30)	411	28.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1431	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	194	13.6%
e	127	8.9%
m	108	7.5%
t	107	7.5%
c	97	6.8%
h	92	6.4%
s	81	5.7%
0	75	5.2%
d	74	5.2%
n	65	4.5%
Other values (30)	411	28.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1431	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	194	13.6%
e	127	8.9%
m	108	7.5%
t	107	7.5%
c	97	6.8%
h	92	6.4%
s	81	5.7%
0	75	5.2%
d	74	5.2%
n	65	4.5%
Other values (30)	411	28.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1431	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	194	13.6%
e	127	8.9%
m	108	7.5%
t	107	7.5%
c	97	6.8%
h	92	6.4%
s	81	5.7%
0	75	5.2%
d	74	5.2%
n	65	4.5%
Other values (30)	411	28.7%

IP
Text

Distinct	246
Distinct (%)	52.0%
Missing	0
Missing (%)	0.0%
Memory size	3.8 KiB

Length

Max length	15
Median length	14
Mean length	13.082452
Min length	10

Characters and Unicode

Total characters	6188
Distinct characters	11
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	175 ?
Unique (%)	37.0%

Sample

1st row	80.193.51.9
2nd row	177.121.98.139
3rd row	191.39.151.2
4th row	177.98.108.75
5th row	200.193.191.156

Value	Count	Frequency (%)
34.90.109.174	37	7.8%
34.91.221.79	27	5.7%
34.90.201.190	23	4.9%
34.90.47.65	19	4.0%
163.116.233.48	9	1.9%
187.43.131.255	8	1.7%
189.28.48.78	8	1.7%
189.69.81.97	7	1.5%
179.34.50.178	6	1.3%
20.191.65.166	5	1.1%
Other values (236)	324	68.5%

Most occurring characters

Value	Count	Frequency (%)
.	1419	22.9%
1	1074	17.4%
2	608	9.8%
9	509	8.2%
7	475	7.7%
4	462	7.5%
0	402	6.5%
3	401	6.5%
8	314	5.1%
5	293	4.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	6188	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	1419	22.9%
1	1074	17.4%
2	608	9.8%
9	509	8.2%
7	475	7.7%
4	462	7.5%
0	402	6.5%
3	401	6.5%
8	314	5.1%
5	293	4.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	6188	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	1419	22.9%
1	1074	17.4%
2	608	9.8%
9	509	8.2%
7	475	7.7%
4	462	7.5%
0	402	6.5%
3	401	6.5%
8	314	5.1%
5	293	4.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	6188	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	1419	22.9%
1	1074	17.4%
2	608	9.8%
9	509	8.2%
7	475	7.7%
4	462	7.5%
0	402	6.5%
3	401	6.5%
8	314	5.1%
5	293	4.7%

Últ. Atualização
Real number (ℝ)

High correlation

Distinct	454
Distinct (%)	96.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	45734.653

Minimum	45734.325
Maximum	45735.304
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	3.8 KiB

Quantile statistics

Minimum	45734.325
5-th percentile	45734.382
Q1	45734.492
median	45734.594
Q3	45734.824
95-th percentile	45735.003
Maximum	45735.304
Range	0.97934028
Interquartile range (IQR)	0.3312037

Descriptive statistics

Standard deviation	0.21091542
Coefficient of variation (CV)	4.6117203 × 10^-6
Kurtosis	-0.37381822
Mean	45734.653
Median Absolute Deviation (MAD)	0.15314815
Skewness	0.60746512
Sum	21632491
Variance	0.044485316
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
45734.55762	4	0.8%
45734.55723	2	0.4%
45734.55668	2	0.4%
45734.99442	2	0.4%
45734.48097	2	0.4%
45734.553	2	0.4%
45734.5545	2	0.4%
45734.55451	2	0.4%
45734.55494	2	0.4%
45734.55624	2	0.4%
Other values (444)	451	95.3%

Value	Count	Frequency (%)
45734.32454	1	0.2%
45734.32605	1	0.2%
45734.32773	1	0.2%
45734.3375	1	0.2%
45734.33791	1	0.2%
45734.34492	1	0.2%
45734.34674	1	0.2%
45734.3476	1	0.2%
45734.36199	1	0.2%
45734.36503	1	0.2%

Value	Count	Frequency (%)
45735.30388	1	0.2%
45735.29727	1	0.2%
45735.28911	1	0.2%
45735.26029	1	0.2%
45735.22545	1	0.2%
45735.22543	1	0.2%
45735.19372	1	0.2%
45735.17926	1	0.2%
45735.135	1	0.2%
45735.07955	1	0.2%

Unnamed: 34
Categorical

High correlation Imbalance

Distinct	2
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	3.8 KiB

45734	449
45735	24

Length

Max length	5
Median length	5
Mean length	5
Min length	5

Characters and Unicode

Total characters	2365
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	45734
2nd row	45734
3rd row	45734
4th row	45734
5th row	45734

Common Values

Value	Count	Frequency (%)
45734	449	94.9%
45735	24	5.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
45734	449	94.9%
45735	24	5.1%

Most occurring characters

Value	Count	Frequency (%)
4	922	39.0%
5	497	21.0%
7	473	20.0%
3	473	20.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2365	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
4	922	39.0%
5	497	21.0%
7	473	20.0%
3	473	20.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2365	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
4	922	39.0%
5	497	21.0%
7	473	20.0%
3	473	20.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2365	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
4	922	39.0%
5	497	21.0%
7	473	20.0%
3	473	20.0%

Interactions

Correlations

	Bonificado	Canal	Cidade	Contratante	Criado	DDD	Estado	ID	ID Anunciante	ID Campanha	ID Cliente	ID Contratante	ID Imóvel	Motivo da Bonificação	Plano	Telefone	Tipo	UTM Campaign	UTM Content	UTM Medium	UTM Source	Unnamed: 34	Últ. Atualização
Bonificado	1.000	0.000	0.000	0.000	0.073	0.000	0.000	0.163	0.000	0.173	0.115	0.000	0.122	1.000	0.000	0.068	0.000	0.158	0.000	0.145	0.109	0.000	0.074
Canal	0.000	1.000	0.148	0.000	0.141	0.134	0.082	0.182	0.349	0.245	0.076	0.000	0.115	0.260	0.588	0.136	0.404	0.577	0.721	0.440	0.436	0.065	0.142
Cidade	0.000	0.148	1.000	1.000	0.220	0.718	0.975	0.161	0.317	0.235	0.000	1.000	0.302	0.000	0.360	0.047	0.301	0.411	0.458	0.159	0.298	0.303	0.219
Contratante	0.000	0.000	1.000	1.000	0.000	0.000	1.000	0.309	0.926	0.740	0.000	1.000	0.408	NaN	1.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000	0.000
Criado	0.073	0.141	0.220	0.000	1.000	-0.025	0.083	1.000	-0.056	-0.111	0.436	0.000	-0.022	0.000	0.051	0.021	0.103	0.284	0.339	0.252	0.246	0.969	0.998
DDD	0.000	0.134	0.718	0.000	-0.025	1.000	0.747	-0.025	0.013	-0.117	0.146	0.000	0.166	0.294	0.137	0.492	0.303	0.353	0.555	0.223	0.176	0.108	-0.019
Estado	0.000	0.082	0.975	1.000	0.083	0.747	1.000	0.086	0.253	0.173	0.000	1.000	0.075	0.000	0.166	0.027	0.245	0.267	0.430	0.099	0.068	0.132	0.078
ID	0.163	0.182	0.161	0.309	1.000	-0.025	0.086	1.000	-0.055	-0.111	0.436	0.309	-0.022	0.000	0.055	0.021	0.123	0.335	0.321	0.262	0.284	0.675	0.998
ID Anunciante	0.000	0.349	0.317	0.926	-0.056	0.013	0.253	-0.055	1.000	0.442	-0.090	0.926	-0.003	0.071	0.810	-0.121	0.363	0.295	0.120	0.186	0.183	0.158	-0.054
ID Campanha	0.173	0.245	0.235	0.740	-0.111	-0.117	0.173	-0.111	0.442	1.000	-0.070	0.740	0.158	0.199	0.342	-0.073	0.104	0.127	0.000	0.056	0.095	0.103	-0.112
ID Cliente	0.115	0.076	0.000	0.000	0.436	0.146	0.000	0.436	-0.090	-0.070	1.000	0.000	0.071	0.612	0.000	0.078	0.126	0.505	0.299	0.060	0.048	0.000	0.433
ID Contratante	0.000	0.000	1.000	1.000	0.000	0.000	1.000	0.309	0.926	0.740	0.000	1.000	0.408	NaN	1.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000	0.000
ID Imóvel	0.122	0.115	0.302	0.408	-0.022	0.166	0.075	-0.022	-0.003	0.158	0.071	0.408	1.000	0.000	0.212	0.075	0.203	0.198	0.342	0.124	0.143	0.000	-0.027
Motivo da Bonificação	1.000	0.260	0.000	NaN	0.000	0.294	0.000	0.000	0.071	0.199	0.612	NaN	0.000	1.000	0.024	0.208	0.233	0.000	0.307	0.261	0.318	0.000	0.000
Plano	0.000	0.588	0.360	1.000	0.051	0.137	0.166	0.055	0.810	0.342	0.000	1.000	0.212	0.024	1.000	0.045	0.436	0.450	0.283	0.388	0.388	0.000	0.061
Telefone	0.068	0.136	0.047	0.000	0.021	0.492	0.027	0.021	-0.121	-0.073	0.078	0.000	0.075	0.208	0.045	1.000	0.185	0.450	0.371	0.275	0.296	0.000	0.025
Tipo	0.000	0.404	0.301	0.000	0.103	0.303	0.245	0.123	0.363	0.104	0.126	0.000	0.203	0.233	0.436	0.185	1.000	0.588	0.587	0.612	0.485	0.020	0.084
UTM Campaign	0.158	0.577	0.411	0.000	0.284	0.353	0.267	0.335	0.295	0.127	0.505	0.000	0.198	0.000	0.450	0.450	0.588	1.000	0.691	0.965	0.965	0.000	0.284
UTM Content	0.000	0.721	0.458	0.000	0.339	0.555	0.430	0.321	0.120	0.000	0.299	0.000	0.342	0.307	0.283	0.371	0.587	0.691	1.000	0.954	0.954	0.312	0.342
UTM Medium	0.145	0.440	0.159	0.000	0.252	0.223	0.099	0.262	0.186	0.056	0.060	0.000	0.124	0.261	0.388	0.275	0.612	0.965	0.954	1.000	0.869	0.134	0.249
UTM Source	0.109	0.436	0.298	0.000	0.246	0.176	0.068	0.284	0.183	0.095	0.048	0.000	0.143	0.318	0.388	0.296	0.485	0.965	0.954	0.869	1.000	0.042	0.245
Unnamed: 34	0.000	0.065	0.303	1.000	0.969	0.108	0.132	0.675	0.158	0.103	0.000	1.000	0.000	0.000	0.000	0.000	0.020	0.000	0.312	0.134	0.042	1.000	0.969
Últ. Atualização	0.074	0.142	0.219	0.000	0.998	-0.019	0.078	0.998	-0.054	-0.112	0.433	0.000	-0.027	0.000	0.061	0.025	0.084	0.284	0.342	0.249	0.245	0.969	1.000

Missing values

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

	ID	ID Cliente	Cliente	E-mail	DDD	Telefone	ID Imóvel	Imóvel	Plano	Cidade	Estado	ID Anunciante	Anunciante	ID Contratante	Contratante	ID Campanha	Campanha	Tipo	Mensagem	Canal	Recibo	Bonificado	Motivo da Bonificação	Elogio	Crítica	Repescagem	Feedback	Criado	UTM Source	UTM Medium	UTM Campaign	UTM Content	IP	Últ. Atualização	Unnamed: 34
0	828769	477917	Juliana Barbour	juliana@skillsonline.com.br	11	950810347	2072	Reserva das Árvores - Paineiras	Brokers	Cotia	São Paulo	484	Imo House Negócios Imobiliários	NaN	NaN	1703.0	Campanha Brokers	Grupo	Olá! Meu nome é Juliana Barbour, email juliana@skillsonline.com.br e telefone (11) 95081-0347.\n \nEu vi o Reserva das Árvores - Paineiras no Apto e me interessei: https://apto.vc/br/sp/cotia/novo-lageado/reserva-das-arvores-paineiras\n \nGostaria de tirar uma dúvida com você. :)	WhatsApp	NaN	Não	NaN	Não	Não	Não	NaN	45734.324537	direct	none	NaN	NaN	80.193.51.9	45734.324537	45734
1	828770	477810	Márcia	marcinha_mg27@hotmail.com	21	988689981	5585	Residencial Pedra de Guaratiba	Business	Rio de Janeiro	Rio de Janeiro	690	MRV	NaN	NaN	2033.0	Campanha Starter	Grupo	Olá! Meu nome é Márcia, email marcinha_mg27@hotmail.com e telefone (21) 98868-9981.\n \nEu vi o Residencial Pedra de Guaratiba no Apto e me interessei: https://apto.vc/br/rj/rio-de-janeiro/guaratiba/residencial-pedra-de-guaratiba\n \nGostaria de tirar uma dúvida com você. :)	WhatsApp	NaN	Não	NaN	Não	Não	Não	NaN	45734.326053	google	cpc	NaN	rio_de_janeiro	177.121.98.139	45734.326053	45734
2	828771	477918	Aline Rodrigues	ali.rodrigues@outlook.com.br	11	976554856	6252	Nostro Mooca	Business	São Paulo	São Paulo	570	Emccamp	NaN	NaN	1920.0	Campanha Starter	Grupo	Olá! Meu nome é Aline Rodrigues, email ali.rodrigues@outlook.com.br e telefone (11) 97655-4856.\n \nEu vi o Nostro Mooca no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/mooca/nostro-mooca\n \nGostaria de tirar uma dúvida com você. :)	WhatsApp	Hypnobox V.3: 871907	Não	NaN	Não	Não	Não	NaN	45734.327708	direct	none	NaN	NaN	191.39.151.2	45734.327731	45734
3	828772	477920	Paloma Herbst	pallomaherbst@gmail.com	11	996957095	4832	Reserva Direcional Limão	Business	São Paulo	São Paulo	714	Direcional Engenharia	NaN	NaN	1161.0	Campanha Starter - São Paulo	Grupo	Olá! Meu nome é Paloma Herbst, email pallomaherbst@gmail.com e telefone (11) 99695-7095.\n \nEu vi o Reserva Direcional Limão no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/limao/reserva-direcional-limao\n \nGostaria de tirar uma dúvida com você. :)	WhatsApp	SalesForce Direcional: 2025-03-18 08:06:00	Não	NaN	Não	Não	Não	NaN	45734.337465	google	cpc	NaN	sao_paulo	177.98.108.75	45734.337500	45734
4	828773	477919	Luiz Fernando	luiz.santos2981@gmail.com	41	997214095	7781	Residencial Turim	Business	Curitiba	Paraná	2382	TNR Construtora	NaN	NaN	1820.0	Campanha Starter	Grupo	Olá! Meu nome é Luiz Fernando, email luiz.santos2981@gmail.com e telefone (41) 99721-4095.\n \nEu vi o Residencial Turim no Apto e me interessei: https://apto.vc/br/pr/curitiba/portao/residencial-turim\n \nGostaria de tirar uma dúvida com você. :)	WhatsApp	NaN	Não	NaN	Não	Não	Não	NaN	45734.337905	chatbot	wpp	pg_busca	chat	200.193.191.156	45734.337905	45734
5	828774	477922	Livia Amorim	amorimdeoliveira@gmail.com	11	989279811	6717	Residencial Ritmos da Barra	Brokers	São Paulo	São Paulo	2055	Aline Teixeira da Silva	NaN	NaN	2001.0	Geração de Leads	Grupo	Olá! Meu nome é Livia Amorim, email amorimdeoliveira@gmail.com e telefone (11) 98927-9811.\n \nEu vi o Residencial Ritmos da Barra no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/barra-funda/residencial-ritmos-da-barra\n \nGostaria de tirar uma dúvida com você. :)	WhatsApp	NaN	Não	NaN	Não	Não	Não	NaN	45734.344919	direct	none	NaN	NaN	200.169.33.46	45734.344919	45734
6	828775	477923	Fátima Thomé	fatithome@hotmail.com	62	981830071	3118	Neo Brooklin	Business	São Paulo	São Paulo	155	Tegra Incorporadora	5.0	Tegra SP	1334.0	Grupo - Tegra SP - Zona Sul	Grupo	NaN	Chat	NaN	Não	NaN	Não	Não	Não	NaN	45734.346736	google	cpc	NaN	neo-brooklin	177.174.219.33	45734.346736	45734
7	828776	477924	Juliano Salles	jotasaamaral@icloud.com	19	989008000	6099	Village Novus	Business	Ribeirão Preto	São Paulo	714	Direcional Engenharia	NaN	NaN	1161.0	Campanha Starter - São Paulo	Grupo	Olá! Meu nome é Juliano Salles, email jotasaamaral@icloud.com e telefone (19) 98900-8000.\n \nEu vi o Village Novus no Apto e me interessei: https://apto.vc/br/sp/ribeirao-preto/jardim-guapore/village-novus\n \nGostaria de tirar uma dúvida com você. :)	WhatsApp	SalesForce Direcional: 2025-03-18 08:20:33	Não	NaN	Não	Não	Não	NaN	45734.347569	direct	none	NaN	NaN	189.76.71.225	45734.347604	45734
8	828777	477929	Camila Brassolatti	caca_brassolatti@hotmail.com	19	983611811	6653	Sensia Taquaral	Business	Campinas	São Paulo	1472	Sensia Incorporadora	NaN	NaN	2076.0	Produto - Sensia Taquaral	Produto	Olá! Meu nome é Camila Brassolatti, email caca_brassolatti@hotmail.com e telefone (19) 98361-1811. Eu vi o Sensia Taquaral no Apto e me interessei: https://apto.vc/br/sp/campinas/taquaral/sensia-taquaral. Gostaria de tirar uma dúvida com você. :)	Mensagem	NaN	Não	NaN	Não	Não	Não	NaN	45734.361991	chatbot	wpp	leadads_produto_t1	mensagem	34.90.201.190	45734.361991	45734
9	828778	255321	Fernanda Azevedo	fercristina301@gmail.com	11	984905042	1438	Square Carapicuíba	Brokers	Carapicuíba	São Paulo	484	Imo House Negócios Imobiliários	NaN	NaN	1703.0	Campanha Brokers	Grupo	Olá! Meu nome é Fernanda Azevedo, email fercristina301@gmail.com e telefone (11) 98490-5042.\n \nEu vi o Square Carapicuíba no Apto e me interessei: https://apto.vc/br/sp/carapicuiba/jardim-das-belezas/square-carapicuiba\n \nGostaria de tirar uma dúvida com você. :)	WhatsApp	NaN	Não	NaN	Não	Não	Não	NaN	45734.365035	chatbot	wpp	home	chat	187.43.130.95	45734.365035	45734

	ID	ID Cliente	Cliente	E-mail	DDD	Telefone	ID Imóvel	Imóvel	Plano	Cidade	Estado	ID Anunciante	Anunciante	ID Contratante	Contratante	ID Campanha	Campanha	Tipo	Mensagem	Canal	Recibo	Bonificado	Motivo da Bonificação	Elogio	Crítica	Repescagem	Feedback	Criado	UTM Source	UTM Medium	UTM Campaign	UTM Content	IP	Últ. Atualização	Unnamed: 34
463	829232	478147	Zoulfikar	kmatizoulfikar@gmail.com	11	913561272	7053	Aria Mooca	Brokers	São Paulo	São Paulo	1794	Claudevania Almeida Morais	NaN	NaN	1582.0	Geração de Leads	Grupo	Olá! Meu nome é Zoulfikar, email kmatizoulfikar@gmail.com e telefone (11) 91356-1272.\n \nEu vi o Aria Mooca no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/mooca/aria-mooca\n \nGostaria de tirar uma dúvida com você. :)	WhatsApp	NaN	Não	NaN	Não	Não	Não	NaN	45735.079549	chatbot	wpp	pgproduto	chat	152.254.129.213	45735.079549	45735
464	829233	478148	Roseli Mascarenhas Santos	roseli.santos1606@gmail.com	11	983072526	8103	Vitta Vila Mariana	Business	São Paulo	São Paulo	1023	Sulplan	NaN	NaN	2069.0	Produto - Vitta Vila Mariana	Produto	Olá! Meu nome é Roseli Mascarenhas Santos, email roseli.santos1606@gmail.com e telefone (11) 98307-2526. Eu vi o Vitta Vila Mariana no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/vila-mariana/vitta-vila-mariana. Gostaria de tirar uma dúvida com você. :)	Mensagem	Apto Connect: Integração externa completada com sucesso. (19/03/2025 03:14:24)	Não	NaN	Não	Não	Não	NaN	45735.134965	facebook	wpp_leadads	bairros	NaN	34.90.47.65	45735.135000	45735
465	829234	478123	Glenda dos Anjos	glendaanjos100@gmail.com	12	982692967	6871	Harmonia	Business	São José dos Campos	São Paulo	970	Árbore Engenharia	NaN	NaN	1436.0	Campanha Starter	Grupo	Olá! Meu nome é Glenda dos Anjos, email glendaanjos100@gmail.com e telefone (12) 98269-2967.\n \nEu vi o Harmonia no Apto e me interessei: https://apto.vc/br/sp/sao-jose-dos-campos/conjunto-residencial-galo-branco/harmonia\n \nGostaria de tirar uma dúvida com você. :)	WhatsApp	CV Leads: ID 170550	Sim	Bonificação automática por fidelidade	Não	Não	Não	NaN	45735.179213	direct	none	NaN	NaN	177.62.127.17	45735.179259	45735
466	829235	478149	Andressa	andressalimmaa@gmail.com	11	974584198	6257	Connect Vila Sônia	Brokers	São Paulo	São Paulo	1991	Anderson de Mello Barbosa	NaN	NaN	1943.0	Geração de Leads	Grupo	Olá! Meu nome é Andressa, email andressalimmaa@gmail.com e telefone (11) 97458-4198.\n \nEu vi o Connect Vila Sônia no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/vila-sonia/connect-vila-sonia\n \nGostaria de tirar uma dúvida com você. :)	WhatsApp	NaN	Não	NaN	Não	Não	Não	NaN	45735.193715	direct	none	NaN	NaN	45.6.35.37	45735.193715	45735
467	829236	478150	Aldecy Diniz	aldecydiniz34@gmail.com	61	993682829	7936	Bosque Santa Cruz by Diálogo Residences	Business	São Paulo	São Paulo	355	Diálogo	NaN	NaN	1916.0	Turbo - Grupo Alto do Ipiranga	Turbo Leads	Olá! Meu nome é Aldecy Diniz, email aldecydiniz34@gmail.com e telefone (61) 99368-2829. Eu vi o Bosque Santa Cruz by Diálogo Residences no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/alto-do-ipiranga/bosque-santa-cruz-by-dialogo-residences. Gostaria de tirar uma dúvida com você. :)	Mensagem	Hypnobox V.3: 1233113	Não	NaN	Não	Não	Não	NaN	45735.225382	facebook	leadads	NaN	1356329168061671	189.28.48.78	45735.225451	45735
468	829237	478150	Aldecy Diniz	aldecydiniz34@gmail.com	61	993682829	7936	Bosque Santa Cruz by Diálogo Residences	Business	São Paulo	São Paulo	355	Diálogo	NaN	NaN	1916.0	Turbo - Grupo Alto do Ipiranga	Turbo Leads	Olá! Meu nome é Aldecy Diniz, email aldecydiniz34@gmail.com e telefone (61) 99368-2829. Eu vi o Bosque Santa Cruz by Diálogo Residences no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/alto-do-ipiranga/bosque-santa-cruz-by-dialogo-residences. Gostaria de tirar uma dúvida com você. :)	Mensagem	Hypnobox V.3: Mensagem duplicada	Sim	Bonificação automática por fidelidade	Não	Não	Não	NaN	45735.225382	facebook	leadads	NaN	1356329168061671	189.28.48.78	45735.225428	45735
469	829238	478152	Laryssa	laryssa.leal2304@gmail.com	11	982544885	7857	Near Guarapiranga	Brokers	São Paulo	São Paulo	1212	Davi Carlos Chaves da Mouraria	NaN	NaN	1752.0	Campanha Brokers	Grupo	Olá! Meu nome é Laryssa, email laryssa.leal2304@gmail.com e telefone (11) 98254-4885.\n \nEu vi o Near Guarapiranga no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/guarapiranga/near-guarapiranga\n \nGostaria de tirar uma dúvida com você. :)	WhatsApp	NaN	Não	NaN	Não	Não	Não	NaN	45735.260289	direct	none	NaN	NaN	170.81.156.100	45735.260289	45735
470	829239	270598	Camila Davanso	camila@davanso.com.br	11	998753517	7398	Bosque Diálogo Jd. Prudência	Business	São Paulo	São Paulo	355	Diálogo	NaN	NaN	1378.0	Grupo - Ongoing	Grupo	Olá! Meu nome é Camila Davanso, email camila@davanso.com.br e telefone (11) 99875-3517. Eu vi o Bosque Diálogo Jd. Prudência no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/jardim-prudencia/bosque-dialogo-jd-prudencia. Gostaria de tirar uma dúvida com você. :)	Mensagem	Hypnobox V.3: 1233128	Não	NaN	Não	Não	Não	NaN	45735.289074	chatbot	wpp	recomendacao_ativa	mensagem	34.90.47.65	45735.289109	45735
471	829240	478153	Gerson Cosme Santos	gersonskill@yahoo.com.br	11	999543659	7255	KA’A Home Boutique	Business	Barueri	São Paulo	508	CNA Spitaletti	NaN	NaN	1837.0	Produto - KAA Home Boutique	Produto	Olá! Meu nome é Gerson Cosme Santos, email gersonskill@yahoo.com.br e telefone (11) 99954-3659. Eu vi o KA’A Home Boutique no Apto e me interessei: https://apto.vc/br/sp/barueri/alphaville/kaa-home-boutique. Gostaria de tirar uma dúvida com você. :)	Mensagem	Apto Connect: Integração externa completada com sucesso. (19/03/2025 07:08:04)	Não	NaN	Não	Não	Não	NaN	45735.297245	chatbot	wpp	leadads_produto_t1	mensagem	34.91.221.79	45735.297269	45735
472	829241	477645	Madalena Tominaga	madalena.tominaga@gmail.com	11	984034484	4803	My Life Vergueiro	Business	São Paulo	São Paulo	2040	Maskan	NaN	NaN	1511.0	Campanha Starter	Grupo	Olá! Meu nome é Madalena Tominaga, email madalena.tominaga@gmail.com e telefone (11) 98403-4484. Eu vi o My Life Vergueiro no Apto e me interessei: https://apto.vc/br/sp/sao-paulo/vergueiro/my-life-vergueiro. Gostaria de tirar uma dúvida com você. :)	Mensagem	Sigavi: MPD: 30117	Não	NaN	Não	Não	Não	NaN	45735.303854	chatbot	wpp	recomendacao_ativa	mensagem	34.91.221.79	45735.303877	45735

Overview

Variables

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values